In the results that follow, `No Loss' or `NL' is the performance of the system without packet loss or quantization, i.e., the performance of the baseline DNN model without splitting into edge and cloud sub-models. `No Completion'or `NC' means no tensor completion, that is, tensor elements from missing packets are simply set to zero. For each loss probability \(P_B \in \lbrace 0.01,0.10,0.20,0.30\rbrace \) , the cloud sub-model Top-1 accuracy performance was accumulated over all average burst lengths and averaged. Tables REF , REF , REF , REF , and REF  summarize Monte Carlo results for all simulation scenarios considered. Fig. REF  shows the Top-1 cloud sub-model classification accuracy over Monte Carlo experiments for EfficientNet-B0 block2b_add tensors. Each marker in Fig. REF  thus represents 140 Monte Carlo runs through the 882 image test set. For all DNNs, SiLRTC and HaLRTC were run for 50 iterations, which were found to be sufficient for convergence in [1]}. Feature recovery done with all packet loss concealment methods leads to significant improvements in Top-1 classification accuracy over the `No Completion case in Fig. REF .
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